Research and Application of a Precise Identification Method for Waterlogged Farmland through Multi-Source Data Integration

The formation of waterlogged areas results from the combined effects of external water accumulation and internal water retention. Accurate identification of such areas is a prerequisite for implementing waterlogged farmland remediation. Existing remote sensing-based identification methods suffer from insufficient coupling of systemic factors and limited recognition accuracy. This study proposes a multi-source data coupling approach for precise waterlogged area identification. The method first utilises high-precision Digital Elevation Models (DEM) to extract topographic depressions. Subsequently, it constructs a Soil Waterlogging Potential Index (SWPI) based on soil texture to identify waterlogging-prone areas. Furthermore, it employs the Soil Water Content Index (SWCI) derived from long-term remote sensing data to identify potential waterlogged areas. Finally, spatial overlay techniques are employed to achieve precise identification of waterlogged areas. Experiments conducted on waterlogged areas within China's Northeast black soil region demonstrate the method's feasibility and accuracy through comparative analysis with traditional remote sensing approaches. This research aims to provide technical support for the conservation and utilisation of black soil farmland in Northeast China.